AI Healthcare System Interface: Explanation Design for Non-Expert User Trust

Larasati, Retno; De Liddo, Anna and Motta, Enrico (2021). AI Healthcare System Interface: Explanation Design for Non-Expert User Trust. In: ACMIUI-WS 2021: Joint Proceedings of the ACM IUI 2021 Workshops (Glowacka, Dorota and Krishnamurthy, Vinayak eds.), CEUR Workshop Proceedings, 2903.



Research indicates that non-expert users tend to either over-trust or distrust AI systems. This raises concerns when AI is applied to healthcare, where a patient trusting the advice of an unreliable system, or completely distrusting a reliable one, can lead to fatal incidents or missed healthcare opportunities. Previous research indicated that explanations can help users to make appropriate judgements on AI Systems' trust, but how to design AI explanation interfaces for non-expert users in a medical support scenarios is still an open research challenge. This paper explores a stage-based participatory design process to develop a trustworthy explanation interface for non-experts in an AI medical support scenario. A trustworthy explanation is an explanation that helps users to make considered judgments on trusting (or not) and AI system for their healthcare. The objective of this paper was to identify the explanation components that can effectively inform the design of a trustworthy explanation interface. To achieve that, we undertook three data collections, examining experts' and non-experts' perceptions of AI medical support system's explanations. We then developed a User Mental Model, an Expert Mental Model, and a Target Mental Model of explanation, describing how non-expert and experts understand explanations, how their understandings differ, and how it can be combined. Based on the Target Mental Model, we then propose a set of 14 explanation design guidelines for trustworthy AI Healthcare System explanation, that take into account non-expert users needs, medical experts practice, and AI experts understanding.

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